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MND NewsWire features plain and simple interpretations of industry related data and events written in a manner that maintains the interest of random readers while still catering to the perspective of a housing market professional.

Two Federal Reserve economists have developed
a home price index which they claim operates in a more real time environment
than those indices currently accepted as the industry standard. Rather than base their index on sales prices,
Elliot Anenberg and Steven Laufer have constructed theirs using a repeat-sales
approach but relying on listing data. Using Data on Seller Behavior to Forecast Short-run House Price Changes was published as part of the Fed's Financial and
Economics Discussion Series.

The paper notes that home price changes have consequences
for the economy as they affect both household wealth and an owner's ability to
borrow. But information on those prices,
unlike that of other assets such as stocks, are reported with a significant
time lag. The Case-Shiller house price
index contains information from several months earlier but has an immediate
effect on home building related stock prices and it is likely that the prices
indexes also have an impact on individual homeowners, policy makers, lenders,
and others. By using information that was available
at the time of the
contract negotiations, the authors designed their list-price index to mitigate
this information friction.

The authors
attribute the time lag in producing the traditional indices to a lack of
incentive for buyer or seller to publicize the price once they have negotiated
an agreement. Even once the price is
disclosed at closing there is typically another lengthy delay before the public
record becomes available. In contrast,
before the contract is signed the seller has a strong incentive to broadcast
the current asking price as a marketing tool.
Thus information on listing prices is disseminated through platforms
such the Multiple Listing Services (MLS) in real time. Once a sale agreement is reached those
listings are removed. The authors
theorize that using the information on home prices before those homes are
delisted could allow them to learn about the level of sale prices much earlier
than what is currently available.

The authors
developed a new house price index reproducing the Case-Shiller repeat-sales
index, substituting sales prices with an estimate based on the final list
prices of all homes that are delisted.
Key to the methodology is associating each delisting with the most
recent prior sale of that property, creating a pair of observations analogous
to a pair of repeat sales in the Case-Shiller and other similar indices. This allows a timelier index of price trends
while maintaining the most attractive feature of other indices; their ability
to control for changes in the mix of homes sold over time by partialing out a
house-specific effect from each price.

Testing their theory was complicated because the
sale-to-list price ratio varies, both in the cross section and across time and
because many delistings are done for reasons other than a sale. The authors found that some of these
variations could be explained by other observable information about seller
behavior such as the time on market (TOM) and the history of list price
changes. This information was used to
adjust the final list price up or down.

The index was tested by using micro data from three large
metropolitan areas, Phoenix, Seattle, and Los Angeles, over the period 2008-2012. They found their index could account for
hetroskedastic errors (i.e. homes with a longer interval between sales should
be downweighted because the likelihood of unobserved changes to house quality
are higher). It could also account for
value weighting - that more valuable homes comprise a larger share of a real
estate portfolio and thus their appreciation/depreciation rates should be given
more weight. The index also accurately
forecasts the Case-Shiller index several months in advance, outperforms
forecasting models that do not use listings data, and for the one metropolitan
area in which data on futures contracts are available, outperforms the market's
expectations as inferred from prices on Case-Shiller future contracts.

The
second set of data was micro data on home listings with dates from which can be
derived the TOM. There is no data to
indicate the reason for delisting or whether, if it were delisted because of a
sale, no information on the terms of that sale.
The listing also includes the specific property address and some
information on the home's characteristics.
Each home was linked by address to its previous sales record.

All three cities in the sample experienced significant declines
in house prices
during the beginning of the sample
period, although the magnitude
of the decline varied considerably across cities The sample period also covers time during
which the homebuyer tax credit was in effect and the 2012 beginning of price
recovery in the cities, all three of which are covered by the Case-Shiller
20-City Index. The authors identified
nearly a million properties that were delisted during the sample period and
which they could link to a previous transaction record. A majority
of listings are delisted without a list price change. The median TOM is between one and two months.
Many delistings are relisted soon after delisting: 20 percent
of delistings are relisted within less than a month and 17 percent of are relisted
between 2 and 6 months later. Many of these relistings may be due to sales agreements that fall through
because a mortgage
contingency fails or an inspection fails.

The authors derived
two index models. In the first, which
they called the simple-list price index they used the same regression equation
as Case-Shiller except for the months where that index was not yet available
and they substituted sales prices with the final list prices of delistings that
were expected to close in a month. For
the previous sale, they used the house price level calculated from the transaction data alone rather than re-estimating it using both transactions and listings data.

They found
that, despite the extreme changes in housing market conditions over the sample
period the sale-to-list price ratio fluctuated within a band of only several percent
but that variation does appear to be correlated with the house price cycle;
periods of rising prices tend to have high sales-to-list price ratios. Another potential source of bias was the
inclusion of all delistings rather than just those that led to sale. Delistings that led to sales tended to have
lower list prices and the magnitude of that price difference was negatively correlated with the house price cycle. This share is also volatile
over time, with hotter markets being associated with a higher probability of sale, suggesting that including all delistings, rather than only the ones that result in sales, will bias the index due
to selection.

On average,
there is a delay of about six weeks between delisting and closing. The distribution of delays does not change much over time. This suggests that the assumption of a time-invariant distribution seems very reasonable, especially since the index is
calculated as a moving
average of the previous three months.

The second model, the adjusted list
price index, attempted to eliminate problems with including all delistings by delivering
predictions for how outcomes would vary by applying observable listing
variables such as time on the market and the list price history. That model
attempts to describe the behavior
of a homeowner trying to sell
her house. It had to take into account
various factors that might influence the outcome such as the value the sellers
place on not selling and staying in the home which may arise from factors such
as employment opportunities or changes in the family's social or financial
situation; time constraints such as the start of the school year or a closing
date on a trade-up home purchase.

The authors considered the ability of
each index to forecast the Case-Shiller HPI at various time horizons - i.e. the
number of weeks from the date of the last observed listings data until the end
of the month they were trying to forecast. At longer horizons an increasing share of the
sales are from properties which have not yet observed delistings. However, even five months into the future
they found their index still had significant predictive power which occurs
become sore transactions take a significant amount of time to close and because
the smoothing process causes sales that close in a given month to affect the
index for the two subsequent months as well.

The adjusted list price Index
performs well, even at 12 weeks. Not
surprisingly performance improves as more listings information about the month
becomes available. Even the Simple
List-Price Index performs well although the adjusted index delivers improved performance
of about 20 percent.

They authors stress that their sample
period covers one of the most volatile time periods in housing history and
Phoenix, one of the most volatile sub-markets.
The fact that our index performs so well during this time period gives us confidence that performance would be as good, or possibly even better, out of sample."

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